Machine Learning is a subset of AI that allows systems to “self-educate”. It is a class of algorithms that, when fed data, can learn to make various kinds of predictions without being programmed to do so. Machine Learning has been around for quite a while, but it’s only recently that it started gaining traction in digital business. Even if you’re not an expert in ML, you’ve likely encountered it. Machine Learning is rapidly becoming the go-to technology in many industries. Businesses heavily invest in ML to stay ahead of their competition through innovation. According to the MIT Technology Review Custom survey, more than half of 375 respondents (60%) from various industries reinforce their companies with ML in one form or another.
Every e-commerce platform today features a simplistic recommendation engine (“also bought with this item”), with Amazon and Walmart using advanced retail analytics and product recommendations on a regular basis. Social media platforms, such as Facebook, Twitter, and LinkedIn also use Machine Learning for personalized content delivery, online advertising, and bidding. The ads that pop up in your Facebook feed are delivered using an ML algorithm that analyzes your preferences to match them with a marketer’s ad delivery settings. The question which might flash through your mind is how your business can benefit from using ML? We will mention a few ways such as:
With consumers’ growing preference for shopping online, criminals have gained an enormous opportunity to commit more fraud. Businesses have employed many types of online security measures but are finding that more are needed. The rise in online transactions also means that many of the measures available to check them make each transaction take longer and slow down the purchase experience — and still often don’t work to stop fraud. The result is increased chargebacks that cost money and impact a brand’s reputation. Luckily, Machine Learning is available to improve the fraud detection process.
Another important aspect, that comes from data classification are improved security measures. AI interference can become another layer of security for the company, as it, based on a large set of data, can identify any possible traits of any possible threats, for example, when it comes to unauthorized access and take measures against them.
If you are in the online retail environment, then you know that your customers like having personalized recommendations delivered to them. It improves the shopping experience in their eyes and offers you a way to sell more products. Machine Learning allows organizations to spade deep into the mindset of their customers. Where some years ago it could include only estimating their browsing habits or delaying their purchase history and referencing it with their demographics, now businesses can see social observations, customer support messages, contracts, and feedback so that more information can be provided along with extensive insights into the mindset of the customer. Machine Learning and predictive analytics are combined to estimate how the users will react so that businesses can provide a reactive experience for each of their customers.
Analyzing Sales Data
The sales function has benefited from the growth in sales-focused data thanks to the increase in digital interaction. Sales teams can tap into metrics from social media platforms, A/B testing, and website visits. Yet with so much data to sift through, sales teams are often bogged down by the time and analysis it takes to pinpoint the necessary insights. Fortunately, Machine Learning can significantly speed up the process of uncovering the most valuable information. Development in the world of Machine Learning has not only given businesses a way to improve how they analyze their huge data – but it also gives them a way to predict what will happen next. Predictive analysis with Machine Learning gives businesses new ways to break their unstructured data, which have a huge number of benefits.
Find Real Time Anomalies
By identifying the outliers in real-time, you can take immediate action. This is particularly useful for fraud analysis (find the fraud as it’s occurring, not a week later), online buying behaviors (keep the customer on your website instead of identifying them after they’ve bought an item somewhere else), and anywhere else where immediate information can drive valuable decisions.
As companies grow, the technology challenges they face become greater. Customer service departments, for example, get bombarded with questions and requests once the firm gets a huge influx of clients. Resolving these tasks, no matter how small and routine they are, requires a substantial amount of manpower. Using ML, firms can reduce call center and mailing costs. The algorithms can help optimize workforce management. Namely, companies can leave routine tasks, such as downgrading a client’s subscription or canceling their account, to AI-powered assistants. Every low-end query (think “How do I reset my password?”) can be solved by a machine directing a client to an appropriate article in your Knowledge Base. This will also contribute to developing self-serving habits among your clients which, too, will drive the support costs down.
Machine Learning algorithms can be used for optimizing software deployment strategies. In particular, you can apply ML to data from DevOps tools, such as Jira or Jenkins, to spot anomalies (long build times, long code check-ins) and thus uncover wastes of software development within a project. One’s behavioral patterns, some say, can be as unique as one’s fingerprints. The algorithms can spot unusual access requests for sensitive repos or automation activities or system provisioning and highlight the “known” malicious activities such as coding back-doors and deploying code that has not been checked and authorized.
Natural Language Processing
There are so many functions where it would be great to have a stand-in to take care of tedious tasks. These include tech support, help desks, customer service, and many others. Thanks to machine learning’s capability for natural language processing (NLP), computers can take over. That’s because NLP provides an automated translation method between computer and human languages. ML focuses on word choices, context, meaning, slang, jargon, and other subtle nuances within human language. As a result, it becomes “more human” in its responses. Using this capability, chatbots can step in and serve as communicators in place of humans for various roles. In addition, NLP applies to situations where there is complex information to dissect, including contracts and research reports.
As these examples show, machine learning is ready to step in and make many business areas more efficient, effective, and profitable. The time to implement the technology of tomorrow is today.